Polynomial classifiers such as pattern recognition systems may include speaker identification systems which typically use pseudo-inverse matrix inversion as part of their speaker training processes. Such training techniques are highly complex, require a large amount of processing resources, and may be mathematically unstable. Furthermore, typical speaker identification training processes are moderately robust and have limited speaker identification performance. Such processes, while being reasonably suited to a small speaker set, become unwieldy for large speaker sets (e.g., greater than 10). Current training processes typically require very large memory storage for a number of very large matrices. Pseudo-inverse matrix inversion is highly inefficient because the memory requirements in the process are at least proportional to the size of the matrix and the number of speakers in the speaker set. As a result, the complexity of the matrix inversion process using pseudo-inverse techniques grows very rapidly as the number of speakers grows.
Thus, what is needed is, an improved speaker training process for speaker identification systems. What is also needed is an improved speaker training process that uses less memory, less processing resources, and is mathematically more stable than typical the pseudo-inverse techniques. What is also needed is a speaker identification system and method having improved speaker identification performance.